Machine Learning-Driven Risk Assessment in Cyber Threat Intelligence: Automating Vulnerability Detection

Abstract

Cyber threat intelligence (CTI) plays a crucial role in mitigating risks and preventing vulnerabilities in network infrastructures. However, the increasing complexity of cyber-attacks has outpaced the capabilities of traditional threat intelligence systems, necessitating more advanced and automated solutions. This paper explores the integration of machine learning (ML) algorithms into CTI for enhancing risk assessment and vulnerability detection. Machine learning techniques such as supervised, unsupervised, and reinforcement learning are examined for their efficacy in automating threat detection, predicting vulnerabilities, and improving decision-making processes in real-time environments. The study also analyzes the strengths and limitations of different ML models, focusing on accuracy, detection speed, and adaptability to new threats. By leveraging data-driven approaches, ML algorithms can significantly reduce human intervention, allowing for faster response times and more accurate assessments of potential risks. This research concludes by discussing the future implications of ML in cyber threat intelligence and the ongoing challenges related to data quality, interpretability, and system scalability.